Methods and apparatus for monitoring gas turbine engine operation

ABSTRACT

A model-based trending process for a gas turbine engine that generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition is described. The engine includes a plurality of sensors that are responsive to engine operations. The trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity. Engine health parameters are estimated and adjusted in a model for component diagnostics and fault detection and isolation. The trend parameters generated are retained.

This invention herein described was made in the course of or under acontract, or a subcontract thereunder, with the United StatesGovernment.

BACKGROUND OF THE INVENTION

This application relates generally to gas turbine engines and, moreparticularly, to methods and apparatus for trending gas turbine engineoperation.

As gas turbine engines operate, the engines may become less efficientdue to a combination of factors including wear and damage. Because therate at which engines deteriorate depends on several operationalfactors, the rate is difficult to predict, and as such, enginecomponents are typically scheduled for maintenance based on apre-selected number of hours or cycles. The pre-selected number istypically conservatively selected based on a number of factors includingpast component experience and past engine health estimates. If acomponent fails, a predetermined diagnosis routine is followed toidentify and replace the failed component.

To estimate engine health and to find engine sensor faults, selectedengine parameters are sensed and monitored to estimate an overall lossin engine performance. Typically, rotor speeds, exhaust gastemperatures, and fuel flows are corrected or normalized for variationsin operating conditions, and these normalized parameters are trended,i.e., their changes over short and long periods of time are plotted, andused to forecast when engine refurbishment is required. Additionally,immediate engine repairs may be scheduled if comparing current trendingvalues to prior trending values illustrates abrupt changes, or stepchanges.

Due to manufacturing tolerances, faults, damage, or deterioration withtime, actual engine characteristics typically are different from theassumed nominal characteristics. Hence, the traditional normalizedparameters may not be accurate. To facilitate improving the estimates ofnormalized sensor parameters as well as of other trended parameters,engine models and parameter estimation algorithms are used to trackengine health and provide “health estimates” of engine components. Knowntrending estimation algorithms account for variations in operatingconditions, but do not account for engine quality and enginedeterioration effects. More specifically, because of the complexity ofthe computations, known correction factors and parameter estimationalgorithms do not provide reliable estimations and trend parametersduring real-time engine operation.

BRIEF SUMMARY OF THE INVENTION

In an exemplary embodiment, a model-based trending process for a gasturbine engine generates, in real-time, engine trend parameters fromengine sensor data and ambient flight condition data to assess enginecondition. The engine includes a plurality of sensors that areresponsive to engine operations. In an exemplary embodiment, thetrending process is implemented using a commercially available processorcoupled to the engine to monitor the engine operations, and having thedesired processing speed and capacity.

The trending process estimates engine health parameters for use in amodel for component diagnostics and fault detection and isolation. Theinteractions and physical relationships of trend parameters within theengine cycle are retained to permit substantially all sensed andmodel-generated virtual parameters for trending to be generatedsimultaneously. As a result, the trending process accounts for enginequality and deterioration effects and provides engine health estimatesthat facilitate improving estimates of performance parameters or“virtual sensors” for use in trending engine operation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating an exemplary embodiment of amodel-based normalization process; and

FIG. 2 is a schematic diagram of an engine model that may be used toestimate sensed parameters with the model-based normalization processillustrated in FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a flow chart illustrating an exemplary embodiment of amodel-based normalization process 10. FIG. 2 is a schematic diagram ofan engine model 12 that may be used to estimate sensed parameters with amodel-based normalization process, such as process 10 shown in FIG. 1.

Although the present invention is sometimes described herein in thecontext of trending the health of an actual aircraft engine 14, itshould be understood that the invention may be used in many othercontexts in which it is desirable to trend the status of actual (i.e.plant) components, as compared to modeling based on a nominal component.In one embodiment, engine 14 is a commercial engine such as a CFM56,CF6, or GE90 engine commercially available from General ElectricCompany, Cincinnati, Ohio. In another embodiment, engine 14 is anindustrial aeroderivative engine such as the LM6000 engine commerciallyavailable from General Electric Company, Cincinnati, Ohio. In yetanother embodiment, engine 14 is a military engine such as the F110 orF414 engine commercially available from General Electric Company,Cincinnati, Ohio.

A flow chart of a process 10 for generating model parameters fortrending is shown in FIG. 1. System 10 could be implemented using, forexample, a commercially available processor (not shown) having thedesired processing speed and capacity. System 10 includes a memorycoupled to the processor, and is coupled to engine 14 to monitor engineoperations.

Engine 14 includes a plurality of sensors (not shown) which monitorengine operation and input 20 real-time actual engine sensor data duringengine operation to engine model 12. In one embodiment, the sensorsmonitor engine rotor speeds, engine temperatures, and engine pressures.Ambient flight condition data is also input 24 to engine model 12. Inone embodiment, ambient flight condition data input 24 includes, but isnot limited to, ambient temperature, ambient pressure, aircraft machnumber, and engine power setting parameters such as fan speed or enginepressure ratio. Collecting ambient flight condition data and actualengine sensor data is known in the art.

Engine model 12 is used to estimate sensed parameters, such as rotorspeeds, temperatures, and pressures, as well as computed parameters suchas thrust, airflows, stall margins, and turbine inlet temperature, basedon environmental conditions, power setting parameters, and actuatorpositions input into engine model 12. In the exemplary embodiment,engine model 12 is a physics-based aero-thermodynamic model 26. Inanother embodiment, engine model 12 is a regression-fit model. In afurther embodiment, engine model 12 is a neural-net model.

Physics-based engine model 26 includes a core engine 28 including inserial, axial flow relationship, a low pressure compressor or boostercompressor 30, a high pressure compressor 32, a combustor or burner 34,a high pressure turbine 36 and a low pressure turbine 38. Core engine 28is downstream from an inlet 40 and a fan 42. Fan 42 is in serial, axialflow relationship with core engine 28 and a bypass duct 44 and a bypassnozzle 50. Fan 42, compressor 30, and low pressure turbine 38 arecoupled by a first shaft 52, and compressor 32 and turbine 36 arecoupled with a second shaft 54. A portion of airflow 58 entering inlet40 is channeled through bypass duct 44 and is exhausted through bypassnozzle 50, and remaining airflow 58 passes through core engine 28 and isexhausted through a core engine nozzle 60.

Engine model 12 is known as a Component Level Model, CLM, because eachcomponent, 28, 44, 50, 42, 60, and 40 within engine model 12 isindividually modeled and then assembled into a specific engine model,such as physics-based engine model 26. Engine model 12 is programmed torepresent a fast-running transient engine cycle that accounts for flightconditions, control variable inputs, and high-pressure compressor bleed.Further, engine model 12 includes tunable parameters such as enginecomponent efficiencies and flows. These parameters can be modified usinga parameter estimation algorithm, thereby modifying the model of anominal or average engine to the model of a specific engine.

After receiving ambient flight condition data and actual engine sensordata input 24 and 20, respectively, model-based trending process 10executes 68 engine model 12 at actual trend conditions using energy andmass balance calculations and a steady-state trim process. The parameterestimation (or tracking) algorithm uses actual sensor data input 20 fromthe engine sensors and model-computed sensor data input after nominalengine model 12 is executed 68 to estimate 70 engine componentefficiencies and flow functions.

The parameter estimation algorithm provides component health parameterestimates in real-time, i.e., on-board engine 14 and during operation.The parameter estimation algorithm is known in the art and may include,but is not limited to a linear regressor or a Kalman filter. Model-basedtrending process 10 then adjusts or fixes 72 component efficiencies andflow functions in engine model 12 to represent the actual enginecomponent health. The component efficiencies and flow functions relateto gas turbine engine major rotating assemblies including fans,compressors, and turbines.

Re-executing 74 engine model. 12 using reference trend conditions, e.g.takeoff operating condition, input 76 to model 12, normalizes data toreference ambient and engine operational conditions and generates 78model parametric data for use with the trending algorithms.

Because model-based normalization process 10 utilizes modelcomputedcorrected sensor parameters and virtual sensors such as thrust,airflows, stall margins, and turbine inlet temperature, trendingparameters are facilitated to be more accurately estimated using process10 than normalized parameters obtained using known trending estimationtechniques that perform simple empirical corrections to sensedparameters. More specifically, model-computed trend alerts such asthreshold exceedences, sudden shifts, or slow drifts are facilitated tobe more accurate and more representative of actual changes in enginehealth using process 10 than are obtainable using other known trendingestimation algorithms.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

What is claimed is:
 1. A method for identifying trends in engineoperation, the engine having a plurality of sensors responsive to engineoperations, said method comprising the steps of: obtaining outputs fromat least some of the sensors when the engine is operating; obtainingambient flight condition data; and using an engine model to generatenormalized engine trend parameters using the engine sensor data andambient flight condition data, wherein engine component efficiencies andflow functions in the engine model are adjusted to account for enginefaults, engine quality, and engine deterioration effects.
 2. A method inaccordance with claim 1 wherein said step of generating normalizedengine trend parameters further comprises the step of: using the enginemodel to generate additional trend parameters or normalized virtualsensors.
 3. A method in accordance with claim 2 wherein said step ofgenerating normalized engine trend parameters further comprising thestep of normalizing engine trend parameters in real-time during engineoperation.
 4. A method in accordance with claim 2 wherein said step ofgenerating normalized engine trend parameters further comprises the stepof using at least one of a physics-based, regression-fit, or aneural-net engine model to normalize engine trend parameters.
 5. Amethod according to claim 2 wherein said step of adjusting enginecomponent efficiencies and flow functions further comprises the step ofusing a parameter estimation algorithm to adjust engine componentefficiencies and flow functions.
 6. A method according to claim 5wherein said step of using a parameter estimation algorithm furthercomprises the step of using at least one of a linear regression schemeor a Kalman filter to adjust engine component efficiencies and flowfunctions.